The availability of genome sequence for a wide variety of experimental organisms and man has challenged us to use this information in the most effective manner. This Core focuses on the application of genome-level analyses in neuroscientific investigation, most based on microarray related technologies, but also supporting a burgeoning use of next-generation sequencing-based approaches, such as RNA sequencing (RNA-seq) and ChlP-seq. For the foreseeable future, expertise in both microarray and sequencing applications need to be maintained within the Core, as both of these technologies are the Core platforms used in functional genomics today. The nervous system poses specific challenges, such as unprecedented tissue heterogeneity and the need to study the genome in relation to circuits and behavior. Further, the analysis of large-scale data sets requires specific expertise in bioinformatics that most neuroscience laboratories lack. It is not practical for most laboratories to become fully proficient in all of the aspects of experimental design and statistics to amplification and hybridization technology that is necessary to perform adequately powered microarray experiments. Furthermore, although Core facilities exist to perform microarray hybridizations, the downstream bioinformatic follow up and statistical support needed to bring a study to completion over a series of months or even years is sorely absent for members of the IDDRC. This is why in the past, many performed such experiments, but many experiments did not lead to publications because the analytic know how was lacking. Further, how to move from a list of genes to biological knowledge poses additional challenges that most laboratories cannot meet alone. The recent advent of high-throughput next-generation sequencing (NGS) is having a major impact in the sciences, especially in biomedical research. Since its introduction to the market in 2005, massively parallel sequencing has dramatically altered genomic research. The degree of throughput and the decreasing cost per base, along with a relatively low error rate, have made it possible to obtain genomic sequence information on a previously unimaginable scale and at a cost that is dramatically lower than that achievable with traditional Sanger sequencing. In addition, these new systems are extending the field to new applications, previously out of reach for conventional sequencing, such as gene expression profiling, small non-coding RNA profiling, structural variant analysis, and analysis of epigenetic modifications of histones and DNA. Soon, whole-genome sequencing will replace existing targeted array technologies and reveal new insights into transcriptomes, genetic and genomic variation, and allow for systematic epigenetic profiling. In particular, RNA-seq is expected to completely change the landscape in the field of gene expression analysis, due to the powerful combination of increased throughput, unprecedented detail, and significantly lower cost. Given the enormous amount of data generated in any NGS experiment, issues related to informatics have been magnified by 1-2 orders of magnitude. Specifically, this is due to massively increased storage and processing capacity for NGS data, as well as the need for mapping reads to the genome for every run. Thus, now more than ever, cores with both data storage, processing and bioinformatics capability are necessary to help support modern neuroscience research.